An application of swarm of quadcopters for searching operations

Meshcheryakov, Roman; Trefilov, Petr; Chekhov, A. V.; Diane, Sekou A.K.; Lesiv, E.A.; Kolodochka, M.A.; Shchukin, K.O.; Novoselskiy, Alexey K.; Гончарова, Е Ю

doi:10.1016/j.ifacol.2019.12.438

Cited by 18 publications

(4 citation statements)

References 6 publications

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“…The heavier the sensing equipment will be, the more thrust would be needed by the UAV, and hence, the power requirements would increase. Specifics of communication : UAVs have to maintain communication with the operators and in some cases, with the control station. A swarm of UAVs with BVLOS capability has the benefit in terms of an efficient control and larger swept area 42 . Furthermore, the acquired data need to be stored onboard and then transmitted to the control station.…”

Section: Robotics For Oandm Of Fowfmentioning

confidence: 99%

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Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends

et al. 2022

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Marine operations required to transfer technicians and equipment represent a significant proportion of the total cost of offshore wind. The profile of sites being considered for floating offshore wind farms (FOWFs), e.g., further from the shore and in harsher environments, indicates that these costs need to be assessed by taking into account the maintenance requirements and restricted weather windows. There is an immediate need to investigate the potential use of robotic systems in the wind farm's operations and maintenance (O&M) activities, to reduce the need for costly manned visits. The use of robotic systems can be critical, not only to replace repetitive activities and bring down the levelised cost of energy but also to reduce the health and safety risks by supporting human operators in performing the desired inspections.This paper provides a review of the state of the art in the applications of robotics for O&M of FOWFs. Emerging technology trends and associated challenges and opportunities are highlighted, followed by an outline of the agenda for future research in this domain.

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Section: Robotics For Oandm Of Fowfmentioning

confidence: 99%

“…A swarm of UAVs with BVLOS capability has the benefit in terms of an efficient control and larger swept area. 42 Furthermore, the acquired data need to be stored onboard and then transmitted to the control station. Here, sufficient data storage and transmission bandwidth need to be taken into account.…”

Section: Specifics Of Communicationmentioning

confidence: 99%

Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends

et al. 2022

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“…For instance, the work in [3] showcases a viable use case of drones in search-and-rescue operations. The drone is designed to fly in forests taking rapid object avoidance into consideration included in real-time route planning.…”

Section: Introductionmentioning

confidence: 99%

MPDrone: FPGA-based Platform for Intelligent Real-time Autonomous Drone Operations

Kovari

Ebeid

2021

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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AI-based autonomous onboard drone applications are evolving rapidly and demand dedicated hardware resources to perform effectively. Currently, CPUs and GPUs are commonly used to run these applications. This paper presents a novel drone platform called MPDrone based on the cutting-edge MPSoC boards that combine FPGA, CPU, and GPU in a single chip. The proposed platform utilizes the reconfigurable FPGA chip to run heavy AI algorithms and the CPU to execute ROS for processing communication with the drone flight controller and onboard sensors. The paper introduces the design and implementation of the MPDrone platform, which is validated in simulation and real-world testing through an intelligent object detection and landing use case. The testing results proved the applicability of the proposed FPGA-based platform for AI applications.

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“…Ржавчина и коррозия могут привести к серьезным проблемам с безопасностью. Следовательно, обнаружение металлических дефектов является серьезной проблемой для обеспечения быстрого, эффективного, но также безопасного осмотра, оценки и обслуживания инфраструктуры [1] и борьбы с явлениями разрушения материалов, которые возникают из-за нескольких факторов, таких, как изменение климата и погодные явления, а также проведения инспекций, в том числе с использованием БПЛА [2]. Современные подходы к анализу изображений для обнаружения дефектов основаны на ограничивающих рамках, размещаемых вокруг дефектных областей, чтобы помочь инженерам быстро сосредоточиться на повреждениях [3] Семантическая информация закодирована в выходном эмбеддинге кодера DeepLabv3, а модуль декодера позволяет детально восстанавливать границы объекта.…”

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Two-step Approach to Corrosion Detection of Metal Structures Using Convolutional Neural Networks When Inspecting Industrial Facilities

Chekhov

2022

Proceedings of the SWSU

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Purpose of research. Corrosion recognition on metal structures is a serious problem in conducting inspections of industrial facilities. Existing approaches to image analysis use all images to recognize areas damaged by corrosion, which is not suitable for structural analysis, since the percentage of errors in this approach is very large. Under conditions of corrosion prediction throughout the image, errors related to predictive mask not on metal structure are possible. Therefore, it is necessary to delete the results of positive class prediction for areas damaged by corrosion but not placed on metal structure. Therefore, in this work, the authors have developed two-step approach for recognizing corrosion of metal structures, thereby achieving the goal of improving recognition accuracy.Methods. We implement two deep learning models focused on Semantic segmentation (DeepLabv3, BiSeNetV2) for corrosion detection that work better in terms of accuracy and time and require fewer annotated samples compared to other deep models, such as Unet, FCN, Mask-RCNN. A new detection approach to metal areas damaged by corrosion, based on the combination of two convolutional neural networks for more accurate pixel prediction by depth architecture models: DeepLabv3 and BiSeNetV2.Results. Experimental studies have calculated the accuracy and F1 measures using FCN, Unet, Mask-RCNN models as well as the proposed approach. Based on obtained results, it was concluded that proposed approach of combining DeepLabv3 and BiSeNetV2 networks increases accuracy and F1 measure for Unet algorithm by 3%, accuracy by 10% and 2% F1 measure for Mask R-CNN and by 12% accuracy and 4% F1 measure for FCN network. Experimental results and comparisons with real data sets confirm the effectiveness of proposed scheme even for very complex images with many different defects. Productivity was assessed based on data annotated by experts.Conclusion. Analyses of existing solutions in the field of recognition of metal structures damaged by corrosion is described. Shortcomings of existing solutions based either on detection of corrosion sites or on pixel segmentation of full image are identified. A new approach to the recognition of metal areas damaged by corrosion based on the combination of two convolutional neural networks for more accurate pixel prediction of DeepLabv3 and BiSeNetV2 is indroduced. Production is evaluated based on data annotated by Precision and F1-score metrics experts.

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An application of swarm of quadcopters for searching operations

Cited by 18 publications

References 6 publications

Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends

Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends

MPDrone: FPGA-based Platform for Intelligent Real-time Autonomous Drone Operations

Two-step Approach to Corrosion Detection of Metal Structures Using Convolutional Neural Networks When Inspecting Industrial Facilities

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